1. Field of the Invention
The invention relates to the field of digital communication and in particular to circuits and methods for correcting for multipath interference or intersymbol interference in digital communication channels.
2. Description of the Prior Art
In any type of communication channel, whether it be by wire or radio transmission, the digital information waveform becomes smeared or spread in time so that the bits or data become at least partially superimposed on each other in the received signal. In the case of cellular telephones, this may occur as a result of the transmitted signal reaching the receiving station by a multiple number of paths, each having substantially different path lengths. In other types of communication channels, data smearing can occur due to bandwidth limitations. To add to the complexity, the physical causes of data smearing in communication channels is time dependent.
Therefore, the prior art has developed equalizers for sorting out the true signal from the data smeared signal. These circuits must be able to perform their iterative functions to converge quickly to a unique solution and to do so in a manner which will rapidly track the time variations of the channel communication characteristics.
The prior art has used recursive least squares methodologies employed in adaptive equalizers for this purpose. See, M. S. Mueller, "Least-Squares Algorithms for Adaptive Equalizers"; Bell Systems Technical Journal, Vol. 60, (October, 1991). However, recursive least-squares methods require some type of training using a known data sequence or a data preamble in combination with a decision directed adaptation. In systems in which there is a time-division multiple access (TDMA) systems such as that proposed for cellular telephones, the channel estimation or equalization problem is particularly difficult, since only a very short preamble is available for equalizer training and the channel characteristics vary greatly from one communication time slot to the next.
The use of Bayesian blind equalizers which approximate the optimum symbol-by-symbol detector for unknown intersymbol interference (ISI) channels is known. For a discussion of the class of algorithms of this type, see for example, R. A. Iltis el.al., "Recursive Bayesian Algorithms for Blind Equalization", Proceedings of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, Calif., pp. 710-15 (November, 1991); and K. Giridhar et.al., "Bayesian/Decision-Feedback Algorithm for Blind Adaptive Equalization"; Optical Engineering, Vol. 31, pp 1211-23 (1992), both incorporated herein by reference. These methodologies employ parallel structures well suited to very large scale integration. Furthermore, the Bayesian equalizers implement the methodology extremely rapidly, e.g. within as few as 20 data symbols when Kalman filter channel estimators are employed, and can track rapid variations (large Doppler spread) in the effective channel coefficients.
In contrast, known blind equalization methodologies, i.e. those that do not require preambles or training, such as constant modulus methodologies as described by J. R. Treichler et.al., "A New Approach to Multipath Correction of Constant Modulus Signals"; IEEE Transactions on Acoust. Speech and Sig. Proc., Vol. ASSP-31, pp. 459-72 (1983) and the Bussgang type techniques typically require thousands of data symbols in order to converge to a unique solution. Furthermore, constant modulus methodologies and the Bussgang methodologies only partially open the eye of the intersymbol interference channel and once the open eye condition is met, the blind equalizer of the prior art must be switched off and a conventional decision feedback equalizer turned on to obtain adequate bit error rate performance. Furthermore conventional decision feedback equalizers are prone to catastrophic error propagation particularly when the channel characteristics are rapidly changing. Still further, conventional decision feedback equalizers are incapable of blind start up and require transmission of the training sequence if a separate blind equalization methodology is not available.
What is needed is a Bayesian blind equalizer which can assume dynamic channel control and provide a performance approaching that of an optimum symbol-by-symbol detector. What is further needed is a Bayesian equalizer that can operate continuously providing both blind start up and tracking of time varying channels.
Therefore, what is needed is some type of blind equalization that can be performed without the use of a preamble or training, which can be effectively implemented even when the communication channel characteristics vary rapidly, and which converges rapidly to a unique decision and solution without a large number of data symbols being required.